ArticleHuman brain mapping2021
Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 26 citations in OpenAlex.
- Temporal stability of functional network configurations and its relationship to cognition and psychopathology in early psychoses.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Automating region selection with genetic algorithms for energy landscape analyses of brain dynamics.Patterns (New York, N.Y.) · 2026Article
- Energy-Based Phase-Locking State Analysis in Brain State Identification.Human brain mapping · 2026Article
- Personalized whole-brain Ising models with heterogeneous nodes capture differences among brain regions.bioRxiv : the preprint server for biology · 2026Article
- Maximum entropy model reveals frequent brain state switching in a multiversal brain function analysis in early psychoses.bioRxiv : the preprint server for biology · 2026Article
- Energy landscape and dynamic state modeling for schizophrenia biomarker extraction in task-based fMRI.PloS one · 2026Article
- From microstates to macroscales: A Critical Review of Maximum Entropy Modeling and Energy Landscape Analysis in functional MRI.Research square · 2025Article
- Noninvasive reduction of neural rigidity alters autistic behaviors in humans.Nature neuroscience · 2025Article
- Comparison of Large Language Model with Aphasia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Linking energetic instability to compositional changes in biological communities.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Energy of Functional Brain States Correlates With Cognition in Adolescent-Onset Schizophrenia and Healthy Persons.Human brain mapping · 2025Article
- Energy landscape analysis based on the Ising model: Tutorial review.PLOS complex systems · 2025Article
- Surrogate data analyses of the energy landscape analysis of resting-state brain activity.Frontiers in neural circuits · 2025Article
- Assessing the impact of hard data patterns on Bayesian Maximum Entropy: a simulation study.Scientific reports · 2024Article
- The alterations of repetitive transcranial magnetic stimulation on the energy landscape of resting-state networks differ across the human cortex.Human brain mapping · 2024Article
- Article
- Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study.bioRxiv : the preprint server for biology · 2024Article
- Energy landscape analysis of brain network dynamics in Alzheimer's disease.Frontiers in aging neuroscience · 2024Article
- Test and re-test reliability of optimal stimulation targets and parameters for personalized neuromodulation.Frontiers in neuroscience · 2023Article
- Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics.Human brain mapping · 2021Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed-effect group-level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior. MEM and its energy landscape properties showed high subject specificity comparable to that of functional connectivity. Canonical correlation analysis identified canonical variables for MEM highly associated with cognitive scores. Inter-individual variations of cognitive scores were also reflected in energy landscape properties such as energies, occupation times, and basin sizes at local minima. We conclude that BMEM provides an efficient method to characterize dynamic properties of individuals using energy landscape analysis of individual brain states.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.